Identifying individuals with tuberculosis with a high risk of onward transmission can guide disease prevention and public health strategies. Here, we train classification models to predict the first sampled isolates in Mycobacterium tuberculosis transmission clusters from demographic and disease data. We find that supervised learning models, in particular balanced random forests, can be used to develop predictive models that discriminate between individuals with TB that are more likely to form transmission clusters and individuals that are likely not to transmit further, with good model performance and AUCs of more than 0.75. We also identified the most important patient and disease characteristics in the best performing classification model, including patient demographics, site of infection, TB lineage, and age at diagnosis. This framework can be used to develop predictive tools for the early assessment of a patient's transmission risk to prioritise individuals for enhanced follow-up with the aim of reducing further transmission.

The authors have declared no competing interest.

This study was funded by the Canadian Institutes of Health Research (Grant No. PJT-159714). J.C.J. is supported by a Michael Smith Foundation for Health Research Scholar Award and CIHR (Grant No. PJT-153213). C.C. is supported by the Michael Smith Foundation for Health Research and the Federal Government of Canada's Canada 150 Research Chair program.

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Ethics committee/IRB of University of British Columbia (certificate H12-00910) gave ethical approval for this work.

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The whole genome sequence data analyzed in the current study are available from the European Nucleotide Archive (ENA) Project number PRJNA413593.